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Erschienen in:
Advanced Methods for 3D Magnetic Localization in Industrial Process Distributed Data-Logging with a Sparse Distance Matrix Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

Multi-objective Algorithms for the Single Machine Scheduling Problem with Sequence-dependent Family Setups

verfasst von : Marcelo Ferreira Rego, Marcone Jamilson Freitas Souza, Igor Machado Coelho, José Elias Claudio Arroyo

Erschienen in: Soft Computing in Industrial Applications

Verlag: Springer International Publishing

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Abstract

This work treats the single machine scheduling problem in which the setup time depends on the sequence and the job family. The objective is to minimize the makespan and the total weighted tardiness. In order to solve the problem two multi-objective algorithms are analyzed: one based on Multi-objective Variable Neighborhood Search (MOVNS) and another on Pareto Iterated Local Search (PILS). Two literature algorithms based on MOVNS are adapted to solve the problem, resulting in the MOVNS_Ottoni and MOVNS_Arroyo variants. Also, a new perturbation procedure for the PILS is proposed, yielding the PILS1 variant. Computational experiments done over randomly generated instances show that PILS1 is statistically better than all other algorithms in relation to the cardinality, average distance, maximum distance, difference of hypervolume and epsilon metrics.

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Vorheriges Kapitel Fuzzy Reliability Analysis of Washing Unit in a Paper Plant Using Soft-Computing Based Hybridized Techniques
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Metadaten
Titel
Multi-objective Algorithms for the Single Machine Scheduling Problem with Sequence-dependent Family Setups
verfasst von
Marcelo Ferreira Rego
Marcone Jamilson Freitas Souza
Igor Machado Coelho
José Elias Claudio Arroyo
Copyright-Jahr
2014
Buch
Soft Computing in Industrial Applications

Print ISBN: 978-3-319-00929-2

Electronic ISBN: 978-3-319-00930-8

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-00930-8_11

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